High-level event identification in social media

The increasing numbers of large data sets generated by information technologies provide a great opportunity to better understand emerging topics in human society. Retrieving real-world events from such data, particularly free-text data, is a complicated task in Natural Language Processing and Location-based Social Networks. In this work, we propose a new approach, which recognizes geo-referenced high-level events/activities mentioned in web sources adopting open gazetteers: OpenStreetMap and Google Maps. Our approach demonstrated on sampled news articles identifies events associated with the relevant topics using a latent Dirichlet allocation. This research is an essential step towards recommendation systems, urban planning, and monitoring.